Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations (readOnlyHint=false, destructiveHint=false), the description discloses the permanence of the binding, its idempotency on repeat calls, the refusal mode for a conflicting version, and the reassurance that it freezes nothing, trains nothing, and spends nothing. That is substantive behavioral context annotations alone could not convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences plus a scope tag; the permanence and no-op/refusal rules are front-loaded, and every clause earns its place with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and only partial annotation coverage, the description fills the gaps an agent needs: the write scope, the permanence semantics, the idempotency and refusal outcomes. Nothing required to call this correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents projectId, datasetId, versionId, and the superadmin-only customerId. The description only adds provenance for versionId (list_dataset_versions); it adds no syntax or format detail beyond the schema, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (bind) and resource (the one frozen dataset version a fine-tuning project trains on), and explicitly contrasts with freezing/training/spending so the agent knows what this does not do. It is easily distinguished from freeze_dataset_version and list_dataset_versions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Names the source of the input (list_dataset_versions), states the permanence rule, and gives explicit when-not behavior: re-binding the same version is a no-op, a different version is refused, and a new project is required to train on another version. This is exactly the routing and exclusion guidance an agent needs.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.